Instructions to use hf-tiny-model-private/tiny-random-XLMForQuestionAnsweringSimple with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-XLMForQuestionAnsweringSimple with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="hf-tiny-model-private/tiny-random-XLMForQuestionAnsweringSimple")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-XLMForQuestionAnsweringSimple") model = AutoModelForQuestionAnswering.from_pretrained("hf-tiny-model-private/tiny-random-XLMForQuestionAnsweringSimple") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e46cfd6a3601209b423897b193b8723535901b75d24ae2e9d66fa1f8e354f233
- Size of remote file:
- 4.21 MB
- SHA256:
- 4736ee515ac15fcae40d98c3aff8731b217f5653b1be816d60de740ce04ae0b0
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.